Search results for "AOT"
showing 10 items of 347 documents
Quantitative study of ANP in hypothalamic supraoptic and paraventricular nuclei in trained rats.
2008
Collision induced decomposition of AOT5YB2+: an unespected intracluster rearrangment
2014
SURFACTANT SELF-ASSEMBLING IN THE GAS PHASE: AOT METAL ION NEGATIVE CHARGED AGGREGATES
2008
A note on higher order Melnikov functions
2005
We present several classes of planar polynomial Hamilton systems and their polynomial perturbations leading to vanishing of the first Melnikov integral. We discuss the form of higher order Melnikov integrals. In particular, we present new examples where the second order Melnikov integral is not an Abelian integral.
Strange Attractors, Chaotic Behavior and Informational Aspects of Sleep EEG Data
1992
In order to perform a nonlinear dimensional analysis of the sleep EEG, we applied an algorithm proposed to calculate the correlation dimension D2 of different sleep stages. D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal under study. An attempt is made to correlate dimensionality analysis and informational aspects of the sleep EEG. Information processing by the brain during different sleep stages of healthy subjects under the influence of lorazepam and in unmedicated acute schizophrenics is estimated.
Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep
1993
Under selected conditions, nonlinear dynamical systems, which can be described by deterministic models, are able to generate so-called deterministic chaos. In this case the dynamics show a sensitive dependence on initial conditions, which means that different states of a system, being arbitrarily close initially, will become macroscopically separated for sufficiently long times. In this sense, the unpredictability of the EEG might be a basic phenomenon of its chaotic character. Recent investigations of the dimensionality of EEG attractors in phase space have led to the assumption that the EEG can be regarded as a deterministic process which should not be mistaken for simple noise. The calcu…
The calculation of the first positive Lyapunov exponent in sleep EEG data
1993
To help determine if the EEG is quasiperiodic or chaotic we performed a new analysis by calculating the first positive Lyapunov exponent L1 from sleep EEG data. Lyapunov exponents measure the mean exponential expansion or contraction of a flow in phase space. L1 is zero for periodic as well as quasiperiodic processes, but positive in case of chaotic processes expressing the sensitive dependence on initial conditions. We calculated L1 for sleep EEG segments of 15 healthy male subjects corresponding to sleep stages I, II, III, IV and REM (according to Rechtschaffen and Kales). Our investigations support the assumption that EEG signals are neither quasiperiodic waves nor simple noise. Moreover…
Nonlinear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent
1995
The generating mechanism of the electroencephalogram (EEG) points to the hypothesis that EEG signals derive from a nonlinear dynamic system. Hence, the unpredictability of the EEG might be considered as a phenomenon exhibiting its chaotic character. The essential property of chaotic dynamics is the so-called sensitive dependence on initial conditions. This property can be quantified by calculating the system's first positive Lyapunov exponent, L1. We calculated L1 for sleep EEG segments of 13 schizophrenic patients and 13 control subjects that corresponded to sleep stages I, II, III, IV and REM (rapid eye movement), as defined by Rechtschaffen and Kales, for the lead positions Cz and Pz. Du…
Nonlinear analysis of sleep eeg in depression: Calculation of the largest lyapunov exponent
1995
Conventional sleep analysis according to Rechtschaffen and Kales (1968) has provided meaningful contributions to the understanding of disturbed sleep architecture in depression. However, there is no characteristic alteration of the sleep cycle, which could serve as a highly specific feature for depressive illness. Therefore, we started to investigate nonlinear properties of sleep electroencephalographic (EEG) data in order to elucidate functional alterations other than those obtained from classical sleep analysis. The application of methods from nonlinear dynamical system theory to EEG data has led to the assumption that the EEG can be treated as a deterministic chaotic process. Chaotic sys…